US2021321955A1PendingUtilityA1
Methods and kits for assessing neurological and ophthalmic function and localizing neurological lesions
Est. expiryJun 17, 2033(~6.9 yrs left)· nominal 20-yr term from priority
Inventors:Uzma Samadani
A61B 3/0041A61B 3/085A61B 5/031A61B 5/7278A61B 3/113A61B 3/0025A61B 5/4064
66
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Claims
Abstract
A method for detecting, diagnosing or screening for concussion in a subject includes a) tracking eye movement of at least one eye of the subject, b) analyzing eye movement of at least one eye of the subject, c) calculating a statistical test for eye movement of at least one eye of the subject as compared to a normal or mean eye movement, and d) detecting, diagnosing or screening for an impairment based on the calculated statistical test.
Claims
exact text as granted — not AI-modified1 - 65 . (canceled)
66 . A method for detecting, diagnosing or screening for concussion in a subject comprising:
a) tracking eye movement of at least one eye of the subject; b) analyzing eye movement of at least one eye of the subject; c) calculating a statistical test for eye movement of at least one eye of the subject as compared to a normal or mean eye movement; and d) detecting, diagnosing or screening for an impairment based on the calculated statistical test.
67 . A method according to claim 66 wherein eye movement of both eyes of the subject are tracked and analyzed.
68 . A method according to claim 66 wherein both x and y coordinates of eye position for one or both eyes of a subject are collected.
69 . A method according to claim 66 wherein the eye movement is tracked for at least about 100 or more seconds.
70 . A method according to claim 66 , further comprising comparing eye movement of at least one eye of the subject to the eye movement of the other eye of the subject or comparing eye movement of at least one eye of the subject to the eye movement of an eye of one or more other subjects or controls.
71 . A method according to claim 66 , further comprising comparing eye movement of both eyes of the subject to the eye movement of one or both eyes of one or more other subjects or controls.
72 . A method according to claim 66 wherein the tracking, analyzing and comparing comprises collecting raw x and y cartesian coordinates of pupil position, normalizing the raw x and y cartesian coordinates, and sorting the data by eye.
73 . A method according to claim 66 wherein the analyzing and comparing comprises calculating one or more individual metric selected from the group consisting of
L ·var Y top=Var( y 1,average k=1:5,1 ) (13)
R ·var Y top=Var( y 2,average k=1:5,1 ) (14)
L ·var X rit=Var( x 1,average k=1:5,2 ) (15)
R ·var X rit=Var( x 2,average k=1:5,2 ) (16)
L ·var Y bot=Var( y 1,average k=1:5,3 ) (17)
R ·var Y bot=Var( y 2,average k=1:5,3 ) (18)
L ·var X lef=Var( x 1,average k=1:5,4 ) (19)
R ·var X lef=Var( x 2,average k=1:5,4 ) (20)
L ·varTotal=Average(Var( x 1,average k=1:5 )+Var( y 1,average k=1:5 )) (21)
R ·varTotal=Average(Var( x 2,average k=1:5 )+Var( y 2,average} (22)
L ·SkewTop=Skew( y 1,average k=1:5,1 ) (27)
R ·SkewTop=Skew( y 2,average k=1:5,1 ) (28)
L ·SkewRit=Skew( x 1,average k=1:5,2 ) (29)
R ·SkewRit=Skew( x 2,average k=1:5,2 ) (30)
L ·SkewBot=Skew( y 1,average k=1:5,3 ) (31)
R ·SkewBot=Skew( y 2,average k=1:5,3 ) (32)
L ·SkewLef=Skew( x 1,average k=1:5,4 ) (33)
R ·SkewLef=Skew( x 2,average k=1:5,4 ) (34)
SkewNorm ( x _ j , k , l ) = Skew ( x _ j , k , l ) σ x _ j , k , l , ( 35 ) SkewNorm ( y _ j , k , l ) = Skew ( y _ j , k , l ) σ y _ j , k , l . ( 36 ) L ·SkewTopNorm=SkewNorm( y 1,average k= 1:5,1) (37)
R ·SkewTopNorm=SkewNorm( y 2,average k= 1:5,1) (38)
L ·SkewRitNorm=SkewNorm( x 1,average k= 1:5,2) (39)
R ·SkewRitNorm=SkewNorm( x 2,average k= 1:5,2) (40)
L ·SkewBotNorm=SkewNorm( y 1,average k= 1:5,3) (41)
R ·SkewBotNorm=SkewNorm( y 2,average k= 1:5,3) (42)
L ·SkewLefNorm=SkewNorm( x 1,average k= 1:5,4) (43)
R ·SkewLefNorm=SkewNorm( x 2,average k= 1:5,4) (44)
BoxHeight j,k =y j,k,1 −y j,k,3 (45)
BoxWidth j,k =x j,k,2 −x j,k,4 (46)
AspectRatio j , k = BoxHeight j , k BoxWidth j , k ( 47 ) BoxArea j,k =BoxHeight j,k ×BoxWidth j,k (48)
Conj
var
Xtop
=
∑
(
x
^
1
)
2
-
0
∑
x
^
1
,
(
57
)
Conj
var
Xrit
=
∑
(
x
^
2
)
2
-
0
∑
x
^
2
,
(
58
)
Conj
var
Xbot
=
∑
(
x
^
3
)
2
-
0
∑
x
^
3
,
(
59
)
Conj
var
Xlef
=
∑
(
x
^
4
)
2
-
0
∑
x
^
4
,
(
60
)
Conj
var
Ytop
=
∑
(
y
^
1
)
2
-
0
∑
y
^
1
,
(
61
)
Conj
var
Yrit
=
∑
(
y
^
2
)
2
-
0
∑
y
^
2
,
(
62
)
Conj
var
Ybot
=
∑
(
y
^
3
)
2
-
0
∑
y
^
3
,
(
63
)
Conj
var
Yrit
=
∑
(
y
^
4
)
2
-
0
∑
y
^
4
,
(
64
)
Conj
Corr
XYtop
=
∑
x
^
1
y
^
1
∑
x
^
1
-
1
,
(
65
)
Conj
Corr
XYrit
=
∑
x
^
2
y
^
2
∑
x
^
2
-
1
,
(
66
)
Conj
Corr
XYbot
=
∑
x
^
3
y
^
3
∑
x
^
3
-
1
,
(
67
)
Conj
Corr
XYlef
=
∑
x
^
4
y
^
4
∑
x
^
4
-
1
(
68
)
74 . A method according to claim 66 wherein the analyzing and comparing comprises calculating one or more individual metric selected from the group consisting of L height, L width, L area, L varXrit, L varXlef, L varTotal, R height, R width, R area, R varYtop, R varXrit, R varXlef, R varTotal, Conj varX, Conj varXrit, Conj varXbot, Conj varXlef and Conj varYlef.Join the waitlist — get patent alerts
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